Search Results for author: Joe Barrow

Found 14 papers, 4 papers with code

Chain of Logic: Rule-Based Reasoning with Large Language Models

1 code implementation16 Feb 2024 Sergio Servantez, Joe Barrow, Kristian Hammond, Rajiv Jain

We introduce a new prompting method, Chain of Logic, which elicits rule-based reasoning through decomposition (solving elements as independent threads of logic), and recomposition (recombining these sub-answers to resolve the underlying logical expression).

Legal Reasoning

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?

1 code implementation ACL 2021 Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, John P. Lalor, Robin Jia, Jordan Boyd-Graber

While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models).

Bias and Fairness in Large Language Models: A Survey

1 code implementation2 Sep 2023 Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, Nesreen K. Ahmed

Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere.

counterfactual Fairness

AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models

1 code implementation23 Oct 2023 Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, Tong Sun

Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks.

Adversarial Attack Blocking

Mitigating Noisy Inputs for Question Answering

no code implementations8 Aug 2019 Denis Peskov, Joe Barrow, Pedro Rodriguez, Graham Neubig, Jordan Boyd-Graber

We investigate and mitigate the effects of noise from Automatic Speech Recognition systems on two factoid Question Answering (QA) tasks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +6

MATERIALizing Cross-Language Information Retrieval: A Snapshot

no code implementations LREC 2020 Petra Galuscakova, Douglas Oard, Joe Barrow, Suraj Nair, Shing Han-Chin, Elena Zotkina, Esk, Ramy er, Rui Zhang

At about the midpoint of the IARPA MATERIAL program in October 2019, an evaluation was conducted on systems{'} abilities to find Lithuanian documents based on English queries.

Information Retrieval Retrieval

It Takes Two to Lie: One to Lie, and One to Listen

no code implementations ACL 2020 Denis Peskov, Benny Cheng, Ahmed Elgohary, Joe Barrow, Cristian Danescu-Niculescu-Mizil, Jordan Boyd-Graber

Trust is implicit in many online text conversations{---}striking up new friendships, or asking for tech support.

Syntopical Graphs for Computational Argumentation Tasks

no code implementations ACL 2021 Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, Henning Wachsmuth

Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection.

Stance Detection

PDFTriage: Question Answering over Long, Structured Documents

no code implementations16 Sep 2023 Jon Saad-Falcon, Joe Barrow, Alexa Siu, Ani Nenkova, David Seunghyun Yoon, Ryan A. Rossi, Franck Dernoncourt

Representing such structured documents as plain text is incongruous with the user's mental model of these documents with rich structure.

Question Answering Retrieval

Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes

no code implementations3 Feb 2024 Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Tong Yu, Hanieh Deilamsalehy, Ruiyi Zhang, Sungchul Kim, Franck Dernoncourt

Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases.

Text Generation Zero-Shot Learning

How Much Annotation is Needed to Compare Summarization Models?

no code implementations28 Feb 2024 Chantal Shaib, Joe Barrow, Alexa F. Siu, Byron C. Wallace, Ani Nenkova

Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace.

News Summarization Text Generation

Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scores

no code implementations1 Mar 2024 Chantal Shaib, Joe Barrow, Jiuding Sun, Alexa F. Siu, Byron C. Wallace, Ani Nenkova

The applicability of scores extends beyond analysis of generative models; for example, we highlight applications on instruction-tuning datasets and human-produced texts.

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